Artificial intelligence is already being put to use in the NHS, with Google's AI firm DeepMind providing technology to help monitor patients. And a new study suggests that Google could soon be meeting with Genomic England - a company set up by the Department of Health to sequence 100,000 genomes – to discuss whether DeepMind could get involved. In an article for The Conversation, Edward Hockings a researcher at the University of the West of Scotland, explains the risks of letting a private company gain access to sensitive genetic data. In Google's case, he says, it could allow them to target users with personalised advertising based on their preferences and health risks. It could also create profiles of people based on their DNA data, which may provide details such as their risk of becoming a criminal.
The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.
Artificial Intelligence (AI) technology can produce improved digital biomarkers of ageing and frailty via gathering physical activity data from smartphones and other wearables, a new study suggests. According to the researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT), AI is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition and other fields. "Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," said co-author Peter Fedichev, Science Director at GERO. "Inspired by these examples, we explored AI potential for'Health Risks Assessment' based on human physical activity," Fedichev added. For the study, published in the journal Scientific Reports, researchers analysed physical activity records and clinical data from a large 2003-2006 US National Health and Nutrition Examination Survey (NHANES). They trained neural network to predict biological age and mortality risk of the participants from one-week long stream of activity measurements.
IMAGE: This is a screenshot of the Gero Lifespan app. Moscow, March 29, 2018 - Researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT) have shown that physical activity data acquired from wearables can be used to produce digital biomarkers of aging and frailty. Many physiological parameters demonstrate tight correlations with age. Various biomarkers of age, such as DNA methylation, gene expression or circulating blood factor levels could be used to build accurate «biological clocks» to obtain individual biological age and the rate of aging estimations. Yet large-scale biochemical or genomic profiling is still logistically difficult and expensive for any practical applications beyond academic research.
The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems under challenging constraints, with limited tools to help them make data driven decisions. In this work we frame the process of finding an optimal malaria policy as a stochastic multi-armed bandit problem, and implement three agent based strategies to explore the policy space. We apply a Gaussian Process regression to the findings of each agent, both for comparison and to account for stochastic results from simulating the spread of malaria in a fixed population. The generated policy spaces are compared with published results to give a direct reference with human expert decisions for the same simulated population. Our novel approach provides a powerful resource for policy makers, and a platform which can be readily extended to capture future more nuanced policy spaces.